A Pragmatist Approach to Complexity Theorizing in Project Studies: Orders and Levels
Bibliographic record
Abstract
The limitations of complexity theorizing in project studies are traced back to simplistic and reductionist theorizing strategies. This article offers pragmatist recommendations to develop strong theorizing strategies organized in a triad: orders of theorizing (degree of recursiveness of the theorizing process), levels of theorizing (interactions between micro, meso, and macro loci of analysis), and the integration between orders and levels brought together in a recursive relationship of co-construction. We offer four main contributions to complexity theorizing in project studies: pragmatism is useful, deeper attention should be paid to theorizing across levels, third-order theorizing is needed, and complexifying terminology is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.067 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".